In climate science several techniques are adopted to compensate for
the lack of data. The general idea is to extract informations about
rare, unobserved events from the limited but available statistics
of less rare, observed events. Purely statistical approaches are
usually framed in the context of extreme value theory [1]}, [2]}, [3]}.
Stochastic weather generators provide to some extent a hybrid statistical-dynamical
approach [4]}, [5]}. A new approach entirely
based on the dynamics of numerical models was proposed in [6]},
where we have introduced the use of rare event algorithms to improve
the sampling efficiency of climate models. These techniques allow
to increase the number of extreme events observed for a given computational
cost, by generating trajectories that are real solutions of the equations
of the model, without additional statistical assumptions.
